Quantitative modeling of the impact of facemasks and associated leakage on the airborne transmission of SARS-CoV-2.

ABSTRACT

The ongoing worldwide outbreak of COVID-19 has set personal protective equipment in the spotlight. A significant number of countries impose the use of facemasks in public spaces and encourage it in the private sphere. Even in countries where relatively high vaccination rates are achieved at present, breakthrough infections have been frequently reported and usage of facemasks in certain settings has been recommended again. Alternative solutions, including community masks fabricated using various materials, such as cotton or jersey, have emerged alongside facemasks following long-established standards (e.g., EN 149, EN 14683). In the present work, we present a computational model to calculate the ability of different types of facemasks to reduce the exposure to virus-laden respiratory particles, with a focus on the relative importance of the filtration properties and the fitting on the wearer's face. The model considers the facemask and the associated leakage, the transport of respiratory particles and their accumulation around the emitter, as well as the fraction of the inhaled particles deposited in the respiratory system. Different levels of leakages are considered to represent the diversity of fittings likely to be found among a population of non-trained users. The leakage prevails over the filtration performance of a facemask in determining the exposure level, and the ability of a face protection to limit leakages needs to be taken into account to accurately estimate the provided protection. Filtering facepieces (FFP) provide a better protection efficiency than surgical and community masks due to their higher filtration efficiency and their ability to provide a better fit and thus reduce the leakages. However, an improperly-fitted FFP mask loses a critical fraction of its protection efficiency, which may drop below the protection level provided by properly-worn surgical and community masks.